一种“数据驱动的不确定性”计算方法来模拟和预测摩擦系统的不稳定性。

Farouk Maaboudallah, Noureddine Atalla
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引用次数: 0

摘要

近年来发展起来的预测摩擦系统不稳定性的方法大多是将随机算法与有限元方法相结合。他们使用随机变量通过标准概率定律来模拟输入参数的不确定性。尽管目前已有先进的数值格式,但仍缺乏一种系统准确的方法来精细描述模型上游的不确定性,从而预测其响应。在这篇贡献中,我们提出了一个数据驱动的随机有限元方案来预测摩擦系统的动态行为。提出的框架依赖于数据驱动的方法,并使用四个步骤。首先,采用随机平衡设计(RBD)对测量数据进行直接积分,进行不确定度的量化。第二步,对生成的随机数据进行迭代求值,求解摩擦激振问题。在第三步中,以这样一种方式对结果数据进行重新排序,即每个测量输入参数的对应值按升序排列。最后,在重排序结果上引入傅里叶谱来计算灵敏度指标。因此,代替基于蒙特卡罗的形式主义或傅立叶振幅灵敏度测试(FAST),该方法的计算成本在样本数量为N的情况下保持在O (N)。我们研究了所建议的求解器在减速制动系统上的效率。总的来说,与文献中可用的方法相比,建议的程序在大大减少的计算时间内实现了出色的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A "data-driven uncertainty" computational method to model and predict instabilities of a frictional system.

Most of the recently developed methods for predicting instabilities of frictional systems couple stochastic algorithms with the finite element method (FEM). They use random variables to model the uncertainty of input parameters through standard probability laws. Regardless of the fact that advanced numerical schemes are available nowadays, a systematic and accurate method to describe finely the uncertainties upstream the model, and thus predict its response is still missing. In this contribution, we present a data-driven stochastic finite element scheme to predict the dynamic behavior of a rubbing system. The proposed framework relies on data-driven approach and uses four steps. In the first, the measured data are integrated directly, for the uncertainty quantification, by means of the random balance design (RBD). In the second step, the generated stochastic data are evaluated in an iterative way to solve friction-induced vibration problem. In the third step, the resulted data are reordered in such a way that the corresponding values of each measured input parameters are ranked in ascending order. Finally, the Fourier spectrum is introduced on the reordered results to compute the sensitivity indices. Thus, instead of Monte Carlo-based formalism or Fourier Amplitude Sensitivity Test (FAST), the computational cost of the proposed method is kept down to O ( N ) with N the number of samples. We investigate the efficiency of the suggested solver on a reduced brake system. Altogether, the suggested procedure achieves excellent accuracy at a much reduced computational time compared to the methods available in the literature.

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来源期刊
Advanced Modeling and Simulation in Engineering Sciences
Advanced Modeling and Simulation in Engineering Sciences Engineering-Engineering (miscellaneous)
CiteScore
6.80
自引率
0.00%
发文量
22
审稿时长
30 weeks
期刊介绍: The research topics addressed by Advanced Modeling and Simulation in Engineering Sciences (AMSES) cover the vast domain of the advanced modeling and simulation of materials, processes and structures governed by the laws of mechanics. The emphasis is on advanced and innovative modeling approaches and numerical strategies. The main objective is to describe the actual physics of large mechanical systems with complicated geometries as accurately as possible using complex, highly nonlinear and coupled multiphysics and multiscale models, and then to carry out simulations with these complex models as rapidly as possible. In other words, this research revolves around efficient numerical modeling along with model verification and validation. Therefore, the corresponding papers deal with advanced modeling and simulation, efficient optimization, inverse analysis, data-driven computation and simulation-based control. These challenging issues require multidisciplinary efforts – particularly in modeling, numerical analysis and computer science – which are treated in this journal.
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